With the continuous development of science and technology, the automatic driving of airplanes and trains has already been realized. However, for automobiles, the progress of automatic driving is far behind the airplanes and trains since the driving environment is relatively complicated. At present, the automatic driving technology for automobiles has become a technology on which various automobile developers has required more attention to develop. Even Google Inc., as a large Internet company, has been researching automatic driving technology for automobiles, and has developed prototype vehicles so far.
The basis for realizing the automatic driving of an automobile is to be able to sense the surrounding driving environment and acquire accurate path information. There are various environment sensing methods in the existing unmanned driving technology, for example, magnetic signal sensing, visual sensing, laser sensing, microwave sensing, communication sensing and the like. However, mainstream path extraction methods include: road navigation by using magnetic signals, visual navigation by using a visible light camera, and environmental restoration by using a laser radar.
A test vehicle with an automatic driving function had been developed in 1995 in Japan, and its method for sensing path information is to embed a magnet wire in the center of a road, provide a magnetic field detection sensor on an automobile, and acquire a travelling path by detecting the magnetic wire. In 2014, the Volvo Corporation announced that it was developing a magnetic navigation system to instruct the unmanned driving. Magnets distributed at fixed intervals were paved on a road, a magnetic sensor was provided on an automobile, and path information was detected by detecting signals from the road magnet arrays in real time, so as to correct the course. Although the path detection based on magnetic signals has the advantage of low degree of interference from the surrounding, path information cannot be extracted since the signal noise is very high when the magnetic sensor detects magnetic signals beyond a certain distance. Meanwhile, during the multilane path detection, the mutual interference of magnetic signals will result in unclear path information. When road navigation is performed by using magnetic signals, the path detection distance is limited, and the detected road environment is limited.
Path detection by visual navigation by using a visible light camera is to acquire two-dimensional or three-dimensional image information of the surroundings of a vehicle by using a camera, and sense the driving environment by an image analysis and recognition technology. In practical applications, the path detection based on this method has complicated background information, requires a complicated image processing algorithm, and is difficult to acquire correct path information by image processing means at night, under severe weather (windy and dusty, heavy fog and rainy) and in a complex illumination environment. For example, first, when a path facing a light source is extracted, the image acquired by the visible light camera may be over-saturated due to the complex illumination, and in this case, it is impossible to acquire the path by image processing. Second, when driving at night, the path obtained by the visible light camera is dark, and the field of view is very limited even if lamps of the automobile or lamps of the camera are used for illumination. Third, in a case of complicated path conditions, there are many roads in the images shot by the visible light camera, and roadside trees are also included in the images, and accordingly, it requires many complicated algorithms and takes a very long period of time to obtain possibly correct results. Thus, the path detection by using the visible light camera does not necessarily obtain correct path results although it requires a series of complicated image processing. When the path information is acquired in this way, both the timeliness and the reliability are low.
The path detection by using laser sensing is to acquire two-dimensional or three-dimensional distance information of the surroundings of a vehicle by using a laser radar, and sense the driving environment by a distance analysis and recognition technology. By using a high-performance laser radar, a 3D topographic map within a certain range can be drawn timely and accurately, and then uploaded to an on-board computer center. In this way, pedestrians, vehicles and barriers to be encountered may be detected. This method is to perform distance measurement and environment reconstruction by emitting laser beams and using the returned information. However, this method, when used in an open environment, is unable to estimate the result of environment reconstruction and thus unable to acquire a correct path. The path detection by using a laser radar has a limited application environment and a high price, and is inconvenient for on-board integration.
In addition to the navigation modes used alone, in order to increase the reliability of path extraction, unmanned vehicles usually employ a combination of different sensing ways for the purpose of judgment. Taking an unmanned vehicle produced by Google as an example, visual sensing, laser sensing and communication sensing are integrated together. Before automatic driving, it is required to record the course of driving an unmanned vehicle by a person in an intended path, and then store it in a remote server. During driving by following the path, on one hand, existing images are processed by visual sensing to analyze the existing path information; meanwhile, by comparing the current image information with the image information stored in the server, it is helpful to judge the real condition of this path. On the other hand, road barriers and background information are judged by a three-dimensional laser scanner. Thus, the unmanned driving system is complicated, time-consuming in processing, and low in operability.
For the path extraction based on the integration of multiple sensors, the sensing system is too complicated, difficult to integrate, high in cost and poor in practicability. Unfortunately, even if the data from multiple sensors is combined, in a case of a complicated weather condition, the unmanned vehicle produced by Google still has problems in terms of path extraction. Liz Gannes, a reporter from Recode and after experiencing the unmanned vehicle produced by Google, said “the sensors for the unmanned vehicle produced by Google have problems in a rainy environment; if it is snowy, more serious problems will arise; and in a heavy fog environment, it is best to drive manually”.
In the field of automatic driving of automobiles, undoubtedly, the acquisition of correct path information during the whole automatic driving process is crucially important. If the path detection is wrong, the diving direction of the automobile will be deviated, and immeasurable losses will be caused. The path detection methods by using magnetic signals, visible light information, laser sensing and the like all have some deficiencies in the technical field of automatic driving. Accordingly, it is very important to provide a simple and reliable path detection method.
The visible light camera is affected in the complex illumination, because the reflected sunlight, scattered sunlight or the like in the path line is shot and the real path information is thus masked. Meanwhile, in the visible light image processing, it is required to remove useless background information and the like from the image, because the shot path line will mislead the result of the path due to the interference from the background information. Since the signal-to-noise ratio increases sharply when the magnetic signals are transmitted beyond a certain distance, and the signal-to-noise ratio is susceptible to interference from an external magnet field and artificial contaminants (e.g., magnets, ores and the like), the path extraction based on magnetic signals has a small amount of acquired path information and is limited in a complicated environment. Thus, the automatic driving field needs a road extraction method which has a high anti-interference performance, a capability of working well under all weather conditions, a large amount of information expressed by the path, a high real-time performance, and an accurate result.
SBUV light signals are light signals in a wave band from 190 nm to 285 nm. The photons irradiated by the sun are isolated by the ozone layer. If the SBUV signals are detected on the earth, they are definitely generated artificially. By using SBUV light signals as markers of a path, the environmental interference is eliminated, and a very high capability of resisting against environmental interference is realized, according to the characteristics of SBUV band. A. SBUV [U1] detector detects light signals only within this wave band, so that it can acquire signals even at night and in a severe weather condition regardless of environmental interferences from different places and different background information. Therefore, the SBUV detector has the advantage of path extraction under any weather condition. A SBUV light signal emitter has a long transmission distance. A milliwatt-level SBUV signal may transmit over 1 km to 3 km. If SBUV detectors have different field of views (FOV), we can get more information about the detected path. In the signal processing procedure, it is only required to process the arranged SBUV light signals, the path detection algorithm is simpler, faster, and the result of path extraction is more accurate without background interference.